Python for Data Analysts
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Find top Python resources from global universities, cool projects, and learning materials for data analytics.

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Python Variables: How to Define/Declare String Variable Types

What is a Variable in Python?
A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for processing.

Python Variable Types
Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables in Python can be declared by any name or even alphabets like a, aa, abc, etc.

How to Declare and use a Variable
Let see an example. We will define variable in Python and declare it as โ€œaโ€ and print it.

1 a=100
2 print (a)
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Python Data Science Handbook

Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.

Creator: Jake Vanderplas
Starsโญ๏ธ: 39k
Fork: 17.1K
Repo: https://github.com/jakevdp/PythonDataScienceHandbook

For more, join https://t.iss.one/pythonanalyst
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Essential NumPy Functions for Data Analysis

Array Creation:

np.array() - Create an array from a list.

np.zeros((rows, cols)) - Create an array filled with zeros.

np.ones((rows, cols)) - Create an array filled with ones.

np.arange(start, stop, step) - Create an array with a range of values.


Array Operations:

np.sum(array) - Calculate the sum of array elements.

np.mean(array) - Compute the mean.

np.median(array) - Calculate the median.

np.std(array) - Compute the standard deviation.


Indexing and Slicing:

array[start:stop] - Slice an array.

array[row, col] - Access a specific element.

array[:, col] - Select all rows for a column.


Reshaping and Transposing:

array.reshape(new_shape) - Reshape an array.

array.T - Transpose an array.


Random Sampling:

np.random.rand(rows, cols) - Generate random numbers in [0, 1).

np.random.randint(low, high, size) - Generate random integers.


Mathematical Operations:

np.dot(A, B) - Compute the dot product.

np.linalg.inv(A) - Compute the inverse of a matrix.

Here you can find essential Python Interview Resources๐Ÿ‘‡
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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Hope it helps :)
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๐Ÿ”ฐ Python if-else demo
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Roadmap to become a Python Developer:

๐Ÿ“‚ Learn Python Basics (Syntax, Data Types, Loops)
โˆŸ๐Ÿ“‚ Learn Data Structures (Lists, Tuples, Dicts, Sets)
โˆŸ๐Ÿ“‚ Learn Functions & Modules
โˆŸ๐Ÿ“‚ Learn File Handling & Exceptions
โˆŸ๐Ÿ“‚ Learn OOP Concepts
โˆŸ๐Ÿ“‚ Learn Libraries (Pandas, NumPy, etc.)
โˆŸ๐Ÿ“‚ Learn Web Development (Flask / Django)
โˆŸ๐Ÿ“‚ Learn APIs & Database Integration
โˆŸ๐Ÿ“‚ Build Projects & Portfolio
โˆŸโœ… Apply for Job

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9 tips to improve your code:

- Declare variables close to usage
- Functions do 1 thing
- Avoid long functions
- Avoid long lines
- Don't repeat code
- Use descriptive variable/function names
- Use few arguments
- Simplify conditions (return age >17;)
- Remove unused code
Without errors, No-one can become a good programmer.
Errors are the most important phase of learning to code.
What are the common built-in data types in Python?

Python supports the below-mentioned built-in data types:

Immutable data types:

๐Ÿ‘‰Number
๐Ÿ‘‰String
๐Ÿ‘‰Tuple

Mutable data types:

๐Ÿ‘‰List
๐Ÿ‘‰Dictionary
๐Ÿ‘‰set
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Python Most Important Interview Questions

Question 1: Calculate the average stock price for Company X over the last 6 months.

Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.

Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.

Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.

Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question

6: Compare the average stock price of Companies X and Z in the first quarter of the year.

#Data#
----------------------------------------------
import pandas as pd
data = {   'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'),   'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180),   'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180),   'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }

df = pd.DataFrame(data)
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โŒจ๏ธ Calculate derivatives in Python
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Pandas Cheatsheet โœ…
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